Instructions to use KKHYA/llavaqwen2.5-32b-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KKHYA/llavaqwen2.5-32b-finetune with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="KKHYA/llavaqwen2.5-32b-finetune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KKHYA/llavaqwen2.5-32b-finetune", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KKHYA/llavaqwen2.5-32b-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KKHYA/llavaqwen2.5-32b-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KKHYA/llavaqwen2.5-32b-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KKHYA/llavaqwen2.5-32b-finetune

SGLang

How to use KKHYA/llavaqwen2.5-32b-finetune with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "KKHYA/llavaqwen2.5-32b-finetune" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KKHYA/llavaqwen2.5-32b-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "KKHYA/llavaqwen2.5-32b-finetune" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KKHYA/llavaqwen2.5-32b-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KKHYA/llavaqwen2.5-32b-finetune with Docker Model Runner:
```
docker model run hf.co/KKHYA/llavaqwen2.5-32b-finetune
```

llavaqwen2.5-32b-finetune / checkpoint-7883 /special_tokens_map.json

KKHYA

Upload KKHYA/llavaqwen2.5-32b-finetune

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	{
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>"
	],
	"eos_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"pad_token": "<\|extra_0\|>",
	"unk_token": {
	"content": "<\|extra_0\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	}

	{
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>"
	],
	"eos_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"pad_token": "<\|extra_0\|>",
	"unk_token": {
	"content": "<\|extra_0\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	}